We use a 4-layer convolutional network as the embedding model \(\phi \)  on CIFAR100 and FEMNIST, and ResNet18 for miniImageNet. As a general framework, we combine PGADA with two classifiers, i.e., ProtoNet and MatchingNet. The hyperparameters are selected by grid search with \(\eta = 1e-3\) , \(b=128\) , \(d = 128\) , \(\lambda _1 =1\) , \(\lambda _2 =1\) , \(\beta =0.5\) , respectively. Note that we also employ transductive batch normalization [1]}. Besides, SGD optimizer [2]} is adopted to train all models in 200 epochs with early stopping. All experiments are implemented in a server with a Intel(R) Core(TM) i9-9820X CPU@3.30GHz, and a GeForce RTX 3090 GPU.
